activity
20232025
most citedComprehensive Study on German Language Models for Clinical and Biomedical Text Understanding

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

A Multimodal Pipeline for Clinical Data Extraction: Applying Vision-Language Models to Scans of Transfusion Reaction Reports

Henning Schäfer, Cynthia S. Schmidt, Johannes Wutzkowsky +7

Despite the growing adoption of electronic health records, many processes still rely on paper documents, reflecting the heterogeneous real-world conditions in which healthcare is d…

eess.IV2024

SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees

Sven Koitka, Giulia Baldini, Cynthia S. Schmidt +11

Traditional segmentation networks approach anatomical structures as standalone elements, overlooking the intrinsic hierarchical connections among them. This study introduces Softma…

cs.CL2024

WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV

Hendrik Damm, Tabea M. G. Pakull, Bahadır Eryılmaz +5

This study aims to leverage state of the art language models to automate generating the "Brief Hospital Course" and "Discharge Instructions" sections of Discharge Summaries from th…

cs.CL2024★ 3 cited

Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding

Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer +17

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate…

cs.CL2024

WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models for Lay Summarization of Scientific Articles

Tabea M. G. Pakull, Hendrik Damm, Ahmad Idrissi-Yaghir +3

This paper details the efforts of the WisPerMed team in the BioLaySumm2024 Shared Task on automatic lay summarization in the biomedical domain, aimed at making scientific publicati…

cs.CL2023★ 1 cited

On the Impact of Cross-Domain Data on German Language Models

Amin Dada, Aokun Chen, Cheng Peng +12

Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have she…